Table REF  shows a comparison between the balanced accuracy and F1-score obtained with our method and previous methods using the same dataset. Both metrics are designed to deal with unbalanced datasets because in these cases, metrics such as accuracy can lead to misinterpretations in the classifier efficiency. Our model showed an improvement in the results obtained by Cruz-Roa et al. [1]}, that presented a custom deep neural network, and the results obtained by Janowczyk and Madabhushi [2]} who used AlexNet architecture [3]} for the classification task in the same dataset.
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